Real-time opportunity discovery for productivity enhancement

ABSTRACT

In an approach for real-time opportunity discovery for productivity enhancement of a production process, a processor extracts a set of features from time series data, through autoencoding using a neural network, based on non-control variables for the time series data. A processor identifies one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data. A processor identifies a neighborhood of a current operational state based on the extracted features. A processor compares the current operational state to historical operational states based on the time series data at the same operational mode. A processor discovers an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood. A processor identifies control variables in the same mode which variables are relevant to the current operational state.

BACKGROUND

The present disclosure relates generally to the field of machinelearning, and more particularly to real-time opportunity discovery forproductivity enhancement of a production process using historical dataencoding.

Many production processes may be quite complex. For example, in an oilsand production, the mined ore may pass through several stages ofextraction, upgrading, and refinement. Similar processes can be found infood and steelmaking production processes. The outflow from an upstreamprocess may become an inflow into a downstream process. Each stage mayinvolve multiple components and processes, and the system is a dynamicsystem. For an oil sand process, in a typical configuration, operationperforms at full capacity when enough raw material is available frommining and all of the components are functioning properly. An upgradingprocess operation may be performed without a vacuum process. A lowproduction mode may result when a quality of bitumen from processed oilsand is low (e.g., with a high concentration of chloride) to avoiddegradation of a coking unit. Operation in partial capability may occurwhen a raw material train line is undergoing maintenance. A lowproduction mode may result when a quality of bitumen from processed oilsand is low (e.g., with a high concentration of chloride) to avoiddegradation of a coking unit. Operation in partial capability may occurwhen a raw material train line is undergoing maintenance.

Traditionally, a majority of bitumen produced is upgraded into syntheticcrude oil before being sold to refineries on the market. However, somebitumen is good enough to send directly to a high-conversion refinerythat has the ability to process heavy/sour crude oil. Such dilutedbitumen example that is sold directly to the refineries includes productfrom in-situ facilities and other places. Petroleum products may beproduced from oil sands through three basic steps: i) extraction of thebitumen from the oil sands, where the solids and water are removed, ii)upgrading of the heavy bitumen to a lighter, intermediate crude oilproduct, and iii) refining of the crude oil into final products such asgasoline, lubricants and diluents. All of these processes involvemultiple sequence steps of physical or chemical transformation toconvert from one material to another. An optimal balance of processes isneeded to reach multiple objectives within such production system. Thereis a need for a plant operator to seek opportunities to enhanceproductivity, for example, less raw materials, less expensive additives,higher final product. There is a further need to focus on specific areaswith a high business value to provide an incremental value in a localstep of manufacturing process. There is yet another need to discoveropportunities for cost, raw material, and energy saving models and helpgain additional profit increasing limited to a local step of an overallplant operation with the opportunities within relative short timewindows in a manufacturing process.

SUMMARY

Certain shortcomings of the prior art are overcome, and additionaladvantages are provided through the provision of an approach forreal-time opportunity discovery for productivity enhancement of aproduction process. Advantageously, a processor extracts a set offeatures from time series data, through autoencoding using a neuralnetwork, based on non-control variables for the time series data. Aprocessor identifies one or more operational modes based on theextracted features including a dimensional reduction with arepresentation learning from the time series data. A processoridentifies a neighborhood of a current operational state based on theextracted features. A processor compares the current operational stateto historical operational states based on the time series data at thesame operational mode. A processor discovers an operational opportunitybased on the comparison of the current operational state to thehistorical operational states using the neighborhood. A processoridentifies control variables in the same mode which variables arerelevant to the current operational state. A processor recommends anaction strategy based on the one or more control variables, the one ormore non-control variables, and a target productivity.

In one or more embodiments, a computer-implemented method is provided tomonitor time series data generated from one or more sensors. Forexample, the time series data can be data from an oil sand operation andproduction process. Petroleum products may be produced from oil sandsthrough several stages, e.g., extraction, upgrading, and refinement.Advantageously, an optimal balance of processes is provided to reachmultiple objectives within such production system.

In one or more embodiments, a computer-implemented method is provided toextract a set of features from the time series data, throughautoencoding using a neural network, e.g., a LSTM auto-encoder, based onone or more non-control variables for the time series data.Advantageously, the LSTM auto-encoder not only uses the set of featuresfor learning, but also learns the set of features through the LSTMauto-encoder. The set of features are information related to the timeseries data. The set of features may be an individual measurableproperty or characteristic of a phenomenon being observed from the timeseries data. An opportunity discovery module may select a subset ofrelevant features for creating a prediction model based on thenon-control variables defining a lack of control by a user on the timeseries data. Advantageously, the opportunity discovery module may reducethe number of resources required to describe the time series data. Theopportunity discovery module may construct combinations of thenon-control variables describing the time series data with sufficientaccuracy through the LSTM auto-encoder. Advantageously, the LSTMauto-encoder may invoke the time series data based auto encoding processto reduce dimensions of a sensor tag spaces to limited embedding space.

In one or more embodiments, a computer-implemented method is provided toidentify one or more operational modes based on the extracted featuresincluding dimension reduction with representation learning from the timeseries data. Advantageously, dimension reduction is a transformation ofthe time series data from a high-dimensional space into alow-dimensional space so that the low-dimensional representation retainssome meaningful properties of the original data, ideally close to theintrinsic dimension. Advantageously, a neighborhood may be identifiedfor the current operational state. The neighborhood may be a dynamicmode within the same operational mode and may be found through Euclideandistances between the historical operational state and the currentoperational state. Advantageously, rather than relying on a rule-basedmode detection that requires lots of prior knowledge and storedprinciples, an automatic process is provided. The opportunity discoverymodule may achieve an opportunity realization through analysis usingunsupervised machine learning. For example, the opportunity discoverymodule may identify an operational opportunity through the comparison ofthe current state with the historical similar operations inside the modeor neighborhood.

In one or more embodiments, a computer-implemented method is provided tocompare a current operational state to a historical operational statebased on the time series data at a same operational mode of the one ormore operational modes with the opportunity discovery module.Advantageously, the opportunity discovery module may identify a specificmode where the current operational state resides. The opportunitydiscovery module may project clusters using t-distributed stochasticneighbor embedding (t-SNE) compression to generate a graph. Theopportunity discovery module may embed high-dimensional points in lowdimensions in a way that respects similarities between points with thet-SNE compression. In an example, the opportunity discovery module mayachieve a high bitumen extraction by comparing the current operationalstate with other operations located at a same mode. The opportunitydiscovery module may analyze episodes with poor performance and maydiscover an operational opportunity to improve.

In one or more embodiments, a computer-implemented method is provided todiscover an operational opportunity based on the comparison of thecurrent operational state to the historical operational state.Advantageously, the operational opportunity may be identified throughthe comparison of the current state with the historical similaroperations inside the mode or neighborhood. In an example, theoperational opportunity may be a set of operational changes deduced fromthe historical operational state to increase the current operationalstate into a higher production in a defined short-term future period,for example, in a two-hour window. In another example, the operationalopportunity may be a set of operational changes deduced from thehistorical operational state to reduce the current operational stateinto a low usage of additives or raw materials in a defined short-termfuture period. Other suitable opportunities are possible to be found.

In one or more embodiments, a computer-implemented method is provided toidentify control variables in the same mode which variables are relevantto the current operational state. Advantageously, the control variablescan be used to calculate rewards (or opportunities) from the episodes ofthe time series data. For example, the control variables may beproduction rates and raw material variables that the user can optimizebased on the best neighboring episodes found. The control variables maybe identified from an established neighborhood of similar historicalnon-control variables to create possible action strategies that arerelevant to the current state based on the time series data.

In one or more embodiments, a computer-implemented method is provided torecommend an action strategy based on the control variables, thenon-control variables, and a target productivity. Advantageously, asimilarity measurement may be defined to identify historical episodesfrom the time series data, with a similar operational state based on thecomparison of the current state with the historical operational state.An episode may be created from the historical episodes that demonstratea higher productivity or throughput. Scores may be generated based onalternative action strategies and may be used to recommend an actionstrategy based on the scoring for each alternative action strategy.

In one or more embodiments, a computer-implemented method is provided tooutput an action strategy for a user. Advantageously, a neighborhoodepisode using a time-stamped chart may be presented in a user interface.An estimated gain of the action strategy may be presented.

In another aspect, a computer program product is provided which includesone or more computer readable storage media, and program instructionscollectively stored on the one or more computer readable storage media.Advantageously, program instructions extract a set of features from timeseries data, through autoencoding using a neural network, based onnon-control variables for the time series data. Program instructionsidentify one or more operational modes based on the extracted featuresincluding a dimensional reduction with a representation learning fromthe time series data. Program instructions identify a neighborhood of acurrent operational state based on the extracted features. Programinstructions compare the current operational state to historicaloperational states based on the time series data at the same operationalmode. Program instructions discover an operational opportunity based onthe comparison of the current operational state to the historicaloperational states using the neighborhood. Program instructions identifycontrol variables in the same mode which variables are relevant to thecurrent operational state. Program instructions recommend an actionstrategy based on the one or more control variables, the one or morenon-control variables, and a target productivity.

In a further aspect, a computer system is provided which includes one ormore computer processors, one or more computer readable storage media,and program instructions stored on the one or more computer readablestorage media for execution by at least one of the one or more computerprocessors. Advantageously, program instructions extract a set offeatures from time series data, through autoencoding using a neuralnetwork, based on non-control variables for the time series data.Program instructions identify one or more operational modes based on theextracted features including a dimensional reduction with arepresentation learning from the time series data. Program instructionsidentify a neighborhood of a current operational state based on theextracted features. Program instructions compare the current operationalstate to historical operational states based on the time series data atthe same operational mode. Program instructions discover an operationalopportunity based on the comparison of the current operational state tothe historical operational states using the neighborhood. Programinstructions identify control variables in the same mode which variablesare relevant to the current operational state. Program instructionsrecommend an action strategy based on the one or more control variables,the one or more non-control variables, and a target productivity.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating an operationopportunity discovery environment, in accordance with an embodiment ofthe present disclosure.

FIG. 2 is a flowchart depicting operational steps of an opportunitydiscovery module within a computing device of FIG. 1, in accordance withan embodiment of the present disclosure.

FIG. 3 illustrates an exemplary functional diagram of the opportunitydiscovery module within the computing device of FIG. 1, in accordancewith an embodiment of the present disclosure.

FIG. 4 illustrates an exemplary architecture diagram of the opportunitydiscovery module within the computing device of FIG. 1, in accordancewith an embodiment of the present disclosure.

FIG. 5 is a block diagram of components of the computing device of FIG.1, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

The present disclosure is directed to systems and methods for real-timeopportunity discovery for productivity enhancement of a productionprocess using historical data encoding.

Embodiments of the present disclosure recognize a need for a plantoperator to seek opportunities to enhance productivity, for example,less raw materials, less expensive additives, higher final product.Embodiments of the present disclosure may focus on specific areas with ahigh business value to provide an incremental value in a local step ofmanufacturing process. Embodiments of the present disclosure maydiscover opportunities for cost, raw material, and energy saving models.The opportunities may have relative short time windows. Embodiments ofthe present disclosure may help gain additional profit increasinglimited to a local step of an overall plant operation. Embodiments ofthe present disclosure may select a complete set of time series fromsensors, e.g., Internet of things (IoT) sensors, such that the timeseries have a complete picture of the plant status. Embodiments of thepresent disclosure may separate the time series into control andnon-control variables. In a production process, embodiments of thepresent disclosure may dynamically provide recommendation and suggestionon enhancing productivities through less raw material consumption, lessexpensive additive usage, less energy consumption, and higher productoutput. Embodiments of the present disclosure may derive more timely andaccurate opportunities in a window of only a few hours. Embodiments ofthe present disclosure may find an operational mode and neighborhoodusing extracted features as embedded space utilizing autoencodingtechniques. The autoencoding techniques may achieve the time-seriesdimension reduction and generate production recommendations from thehistorical similarity analysis in the mode or neighborhood in anembedded space.

Embodiments of the present disclosure may apply a long short-term memory(LSTM) auto-encoder to extract the feature or embedded space fornon-control variables. Embodiments of the present disclosure may definethe neighborhood or use Gaussian mixture clustering applied to anembedded space to identify static operational modes. Embodiments of thepresent disclosure disclose identifying the neighborhood for the currentoperational state. The neighborhood may be a dynamic mode within thesame operational mode and may be found through Euclidean distancesbetween the historical operational state and the current operationalstate. Embodiments of the present disclosure may identify opportunitiesthrough the identification of improvement recommendation by looking atthe difference of the control variables. Embodiments of the presentdisclosure may compare the current operational state with otheroperations located at the same mode. The poor episodes with poorperformance give an opportunity to improve. Embodiments of the presentdisclosure may limit historical episodes by selecting the neighborhoodof the current operational state. Embodiments of the present disclosuremay perform verification of the completion of the time series data forthe performance accuracy of a prediction model developed using all thecontrol and non-control variables.

The present disclosure will now be described in detail with reference tothe Figures. FIG. 1 is a functional block diagram illustrating anoperation opportunity discovery environment, generally designated 100,in accordance with an embodiment of the present disclosure.

In the depicted embodiment, operation opportunity discovery environment100 includes computing device 102, time series data 104, and network108. In an embodiment, time series data 104 may be accessed directly bycomputing device 102. In another embodiment, time series data 104 may beaccessed through a communication network such as network 108. In one ormore embodiments, time series data 104 may be data captured by one ormore sensors. For example, time series data 104 can be data from an oilsand operation and production process. In the example of the oil sandoperation and production process, some bitumen may be upgraded intosynthetic crude oil before being sold to refineries. Some bitumen may begood enough to be delivered to a high-conversion refinery that canprocess heavy crude oil. Petroleum products may be produced from oilsands through several stages, e.g., extraction, upgrading, andrefinement. For example, solid and water may be removed during theextraction stage which may extract bitumen from the oil sands. Duringthe upgrading stage, bitumen may be upgraded to a lighter, intermediatecrude oil product. During the refinement stage, crude oil may be refinedinto final products such as gasoline, lubricants and diluents. Theprocesses during the stages may involve multiple sequence steps ofphysical or chemical transformation to convert from one material toanother. An optimal balance of processes is needed to reach multipleobjectives within such production system.

In another example, time series data 104 can be data from a steelmakingprocess of producing steel from iron ore and/or scrap. Impurities suchas nitrogen, silicon, phosphorus, sulfur and excess carbon may beremoved from the sourced iron, and alloying elements such as manganese,nickel, chromium, carbon and vanadium may be added to produce differentgrades of steel. In yet another example, time series data 104 can bedata from a production process which may process soybeans into soysauces with additives added to the soy sauces. In yet another example,time series data 104 can be data from any other suitable operation andproduction process.

In one or more embodiments, time series data 104 can be data, forexample, including non-control variables 122 and control variables 124.Non-control variables 122 and control variables 124 may be separated toensure the ability to take action to gain production enhancementopportunities. For example, non-control variables 122 may betime-stamped variables and be defined as a set of variables from sensorswhich users have little to no control over. Non-control variables 122may be parameters that are used to define how similar operation andproduction conditions are. Non-control variables 122 may retrieveepisodes from time series data 104. Control variables 124 may betime-stamped variables and be defined as a set of variables from sensorsfor actions that can be controlled by a user. In an example, controlvariables 124 can be used to calculate rewards (or opportunities) fromthe episodes of time series data 104. Control variables 124 may beproduction rates and raw material variables that the user can optimizebased on the best neighboring episodes found. Control variables 124 maybe identified from an established neighborhood of similar historicalnon-control variables 122 to create possible action strategies that arerelevant to the current state based on time series data 104.

In a froth production example, non-control variables 122 may beenvironmental variables that a user may have little or no control over,for example, environmental temperatures, flash, cloud, viscosity,hydrogen availability, coker rate, and virgin production. Controlvariables 124 may be variables for actions that can be controlled by auser, for example, production rates and raw material variables that auser can optimize or change. Control variables 124 may include, forexample, charge into diesel hydrotreating or catalytic hydrogentreating, feedslate into diesel hydrotreating with low vacuum gas oil,low vacuum gas oil, side draw kerosene, heavy naphtha, and cokerkerosene. Diesel hydrotreating or catalytic hydrogen treating may bemainly to reduce undesirable species from straight-run diesel fractionby selectively reacting these species with hydrogen in a reactor atelevated temperatures and at moderate pressures. In order tosuccessfully produce ultra-low-sulfur diesel, organo-sulfur species needto be removed including the substituted dibenzothiophenes and otherrefractory sulfur species. Multiple reactions may occur in parallel onthe diesel hydrotreating catalyst surface includinghydrodesulfurization, hydrodenitrogenation, and aromaticsaturation/hydrogenation. Feeds for a diesel hydrotreating unit may havea nominal distillation range of 300-700° F. Different process design andflow schemes can be employed for diesel hydrotreating depending on theprocess objectives and characteristics of the feed being processed.

In various embodiments of the present disclosure, computing device 102can be a laptop computer, a tablet computer, a netbook computer, apersonal computer (PC), a desktop computer, a mobile phone, asmartphone, a smart watch, a wearable computing device, a personaldigital assistant (PDA), or a server. In another embodiment, computingdevice 102 represents a computing system utilizing clustered computersand components to act as a single pool of seamless resources. In otherembodiments, computing device 102 may represent a server computingsystem utilizing multiple computers as a server system, such as in acloud computing environment. In general, computing device 102 can be anycomputing device or a combination of devices with access to opportunitydiscovery module 110 and network 108 and is capable of processingprogram instructions and executing opportunity discovery module 110, inaccordance with an embodiment of the present disclosure. Computingdevice 102 may include internal and external hardware components, asdepicted and described in further detail with respect to FIG. 5.

Further, in the depicted embodiment, computing device 102 includesopportunity discovery module 110. In the depicted embodiment,opportunity discovery module 110 is located on computing device 102.However, in other embodiments, opportunity discovery module 110 may belocated externally and accessed through a communication network such asnetwork 108. The communication network can be, for example, a local areanetwork (LAN), a wide area network (WAN) such as the Internet, or acombination of the two, and may include wired, wireless, fiber optic orany other connection known in the art. In general, the communicationnetwork can be any combination of connections and protocols that willsupport communications between computing device 102 and opportunitydiscovery module 110, in accordance with a desired embodiment of thedisclosure.

In the depicted embodiment, opportunity discovery module 110 includesLSTM auto-encoder 112, prediction model 114, variable identificationmodule 116, strategy recommendation module 118 and output module 120. Inthe depicted embodiment, LSTM auto-encoder 112, prediction model 114,variable identification module 116, strategy recommendation module 118and output module 120 are located on computing device 102. However, inother embodiments, LSTM auto-encoder 112, prediction model 114, variableidentification module 116, strategy recommendation module 118 and outputmodule 120 may be located externally and accessed through acommunication network such as network 108.

In one or more embodiments, opportunity discovery module 110 isconfigured to monitor time series data 104 generated from one or moresensors. For example, time series data 104 can be data from an oil sandoperation and production process. Petroleum products may be producedfrom oil sands through several stages, e.g., extraction, upgrading, andrefinement. For example, solid and water may be removed during theextraction stage which may extract bitumen from the oil sands. Duringthe upgrading stage, bitumen may be upgraded to a lighter, intermediatecrude oil product. During the refinement stage, crude oil may be refinedinto final products such as gasoline, lubricants and diluents. Theprocesses during the stages may involve multiple sequence steps ofphysical or chemical transformation to convert from one material toanother. An optimal balance of processes is needed to reach multipleobjectives within such production system. In another example, timeseries data 104 can be data from a steelmaking process of producingsteel from iron ore and/or scrap. Impurities such as nitrogen, silicon,phosphorus, sulfur and excess carbon may be removed from the sourcediron, and alloying elements such as manganese, nickel, chromium, carbonand vanadium may be added to produce different grades of steel. In yetanother example, time series data 104 can be data from a productionprocess which may process soybeans into soy sauces with additives addedto the soy sauces. In yet another example, time series data 104 can bedata from any other suitable operation and production process.

In one or more embodiments, opportunity discovery module 110 isconfigured to extract a set of features from time series data 104,through a recurrent neural network, e.g., LSTM auto-encoder 112, basedon one or more non-control variables 122 for time series data 104.Opportunity discovery module 110 may not only use the set of featuresfor learning, but also learn the set of features through LSTMauto-encoder 112. The set of features are information related to timeseries data 104. The set of features may be an individual measurableproperty or characteristic of a phenomenon being observed from timeseries data 104. Opportunity discovery module 110 may select a subset ofrelevant features for creating prediction model 114 based on non-controlvariables 122 defining no control by a user on time series data 104.Opportunity discovery module 110 may reduce the number of resourcesrequired to describe time series data 104. Opportunity discovery module110 may construct combinations of non-control variables 122 describingtime series data 104 with sufficient accuracy through LSTM auto-encoder112. LSTM auto-encoder 112 may be an artificial neural network used tolearn efficient data coding in an unsupervised manner. LSTM auto-encoder112 may be a recurrent neural network with an implementation of anautoencoder for time series data 104 using an encoder-decoder LSTMarchitecture. LSTM auto-encoder 112 may learn a representation(encoding) for time series data 104, e.g., for dimensionality reduction.LSTM auto-encoder 112 may generate from the reduced encoding arepresentation as close as possible to the original input of time seriesdata 104. LSTM auto-encoder 112 may include an encoder and a decoder.The encoder may use raw data (e.g., time series data 104) as input andproduce feature or representation as output, and the decoder uses theextracted feature from the encoder as input and reconstructs theoriginal input raw data as output. Training can be repeated until somestopping criteria are satisfied. LSTM auto-encoder 112 may invoke timeseries data 104 based auto encoding process to reduce dimensions of asensor tag spaces to limited embedding space. LSTM auto-encoder 112 mayinvoke a clustering method to generate potential modes. LSTMauto-encoder 112 may invoke a type of artificial neural network used tolearn efficient data coding in an unsupervised manner. LSTM auto-encoder112 may get a fixed sized vector from time series data 104.

In one or more embodiments, opportunity discovery module 110 isconfigured to identify one or more operational modes based on theextracted features including dimension reduction with representationlearning from time series data 104. Dimension reduction may be atransformation of time series data 104 from a high-dimensional spaceinto a low-dimensional space so that the low-dimensional representationretains some meaningful properties of the original data, ideally closeto the intrinsic dimension. Opportunity discovery module 110 mayidentify a neighborhood for the current operational state. Theneighborhood may be a dynamic mode within the same operational mode andmay be found through Euclidean distances between the historicaloperational state and the current operational state. Opportunitydiscovery module 110 may define the neighborhood or use Gaussian mixtureclustering applied to embedded to identify static operational modes. Amode may include a status of plant operation. A hard category type of amode may regard a specific operational configuration of an entireproduction process, such as manufacturing a pipe configuration, a unitoperation status. A soft category type of a mode may include aproduction level of local or global operations. In the sand oilindustry, there is a complex process of converting sand oil intosynthesis crude oil. To complete a synthesis crude oil productiongeneration, there are multiple stages involved, including primaryextraction, secondary extraction, and upgrading. Each stage involvesmultiple components and processes, and the system is a dynamic system.Some of the involved modes may be explicitly known by a site engineer.Other modes may be hidden and can be identified through advancedanalytic models. Opportunity discovery module 110 may automaticallydetect an operational mode by learning from historical sensor and otherproduction data and may achieve compact feedback in the form of modes.Opportunity discovery module 110 may determine the operational modesalgorithms and models. Opportunity discovery module 110 may use theoperational modes as a benchmark if similar production conditions havebeen present in the past, or if a new mode may be identified. Theoperational modes may be used to recommend better control actions or toautomatically change control parameters. If a new mode is identified,the new mode may be saved to expand the memory and knowledge of thesystem. Opportunity discovery module 110 may compute historicalbenchmarking of a detected mode against historical plant data when theplant is in the same mode. Opportunity discovery module 110 may use thedetected mode as a basis for identifying the similar time periods fromhistory. For instance, time periods from history may be identified whenthe same mode was active. Opportunity discovery module 110 may furtheruse factor analysis on the set of process variables that jointly definethe detected mode to identify the key difference in terms of the currentvariable values versus the historical variable values. Opportunitydiscovery module 110 may compute and display to a site engineer on thepossible root cause of the low levels of operation/production.Opportunity discovery module 110 may provide an analytic approach toidentify or classify modes over a complex manufacture process.Opportunity discovery module 110 may provide a detailed or generalmultivariate mode identification, and as a byproduct, may also achieve apartitioning of un-partitioned data into several subsets. Rather thanrelying on a rule-based mode detection that requires lots of priorknowledge and stored principles, opportunity discovery module 110 mayprovide an automatic process, providing a simpler human involvementapproach to generate modes. Opportunity discovery module 110 may achievean opportunity realization through analysis using unsupervised machinelearning. For example, opportunity discovery module 110 may identify anoperational opportunity through the comparison of the current state withthe historical similar operations inside the mode or neighborhood.

In one or more embodiments, opportunity discovery module 110 isconfigured to compare a current operational state to a historicaloperational state based on the time series data at a same operationalmode of the one or more operational modes. Opportunity discovery module110 may identify a specific mode where the current operational stateresides. Opportunity discovery module 110 may project clusters usingt-distributed stochastic neighbor embedding (t-SNE) compression togenerate a graph. The t-SNE is an algorithm for dimensionality reductionthat is suited to visualizing high-dimensional data, e.g., time seriesdata 104. Opportunity discovery module 110 may embed high-dimensionalpoints in low dimensions in a way that respects similarities betweenpoints with the t-SNE compression. In an example, opportunity discoverymodule 110 may achieve a high bitumen extraction by comparing thecurrent operational state with other operations located at a same mode.Opportunity discovery module 110 may analyze episodes with poorperformance and may discover an operational opportunity to improve.Opportunity discovery module 110 may focus on these operation episodesin the history reached a high froth production and may extract keyoperation actions from these episodes to help the current operation.

In one or more embodiments, opportunity discovery module 110 isconfigured to discover an operational opportunity based on thecomparison of the current operational state to the historicaloperational state. The operational opportunity may be identified throughthe comparison of the current state with the historical similaroperations inside the mode or neighborhood. In an example, theoperational opportunity may be a set of operational changes deduced fromthe historical operational state to increase the current operationalstate into a higher production in a defined short-term future period,for example, in a two-hour window. In another example, the operationalopportunity may be a set of operational changes deduced from thehistorical operational state to reduce the current operational stateinto a low usage of additives or raw materials in a defined short-termfuture period. Other suitable opportunities are possible to be found.

In one or more embodiments, opportunity discovery module 110 isconfigured to identify control variables 124 in the same mode whichvariables are relevant to the current operational state. Controlvariables 124 may define actions that can be controlled by a user.Control variables 124 may be time-stamped variables and can be definedas a set of variables from sensors for actions that can be controlled bya user. In an example, control variables 124 can be used to calculaterewards (or opportunities) from the episodes of time series data 104.For example, control variables 124 may be production rates and rawmaterial variables that the user can optimize based on the bestneighboring episodes found. Control variables 124 may be identified froman established neighborhood of similar historical non-control variables122 to create possible action strategies that are relevant to thecurrent state based on time series data 104. In a froth productionexample, control variables 124 may include, for example, charge intodiesel hydrotreating or catalytic hydrogen treating, feedslate intodiesel hydrotreating with low vacuum gas oil, low vacuum gas oil, sidedraw kerosene, heavy naphtha, and coker kerosene. Diesel hydrotreatingor catalytic hydrogen treating may be mainly to reduce undesirablespecies from straight-run diesel fraction by selectively reacting thesespecies with hydrogen in a reactor at elevated temperatures and atmoderate pressures. In order to successfully produce ultra-low-sulfurdiesel, organo-sulfur species need to be removed including thesubstituted dibenzothiophenes and other refractory sulfur species.Multiple reactions may occur in parallel on the diesel hydrotreatingcatalyst surface including hydrodesulfurization, hydrodenitrogenation,and aromatic saturation/hydrogenation.

In one or more embodiments, opportunity discovery module 110 isconfigured to recommend an action strategy based on control variables124, non-control variables 122, and a target productivity. Opportunitydiscovery module 110 may define a similarity measurement to identifyhistorical episodes from time series data 104, with a similaroperational state based on the comparison of the current state with thehistorical operational state. Opportunity discovery module 110 maycreate an episode from the historical episodes that demonstrate a higherproductivity or throughput. Opportunity discovery module 110 maygenerate scores based on alternative action strategies and may recommendan action strategy based on the scoring for each alternative actionstrategy.

In one or more embodiments, opportunity discovery module 110 isconfigured to output an action strategy for a user. Opportunitydiscovery module 110 may provide a user interface to interface with theuser. Opportunity discovery module 110 may provide other suitable outputways with the user. Opportunity discovery module 110 may provide anindicator or alert to the user for a discovered operational opportunity.Opportunity discovery module 110 may display the operational modes as agraph using a t-SNE method. Opportunity discovery module 110 may displaya neighborhood episode using a time-stamped chart. Opportunity discoverymodule 110 may display an estimated gain of the action strategy.

In one or more embodiments, LSTM auto-encoder 112 is configured toextract a set of features from time series data 104 based on non-controlvariables 122 for time series data 104. In an example, LSTM auto-encoder112 may be an artificial neural network used to learn efficient datacoding in an unsupervised manner. LSTM auto-encoder 112 may be capableof automatically extracting effect of past events. LSTM auto-encoder 112may be a recurrent neural network with an implementation of anautoencoder for time series data 104 using an encoder-decoder LSTMarchitecture. LSTM auto-encoder 112 may learn a representation(encoding) for time series data 104, e.g., for dimensionality reduction.LSTM auto-encoder 112 may generate from the reduced encoding arepresentation as close as possible to the original input of time seriesdata 104. LSTM auto-encoder 112 may include an encoder and a decoder.The encoder may use raw data (e.g., time series data 104) as input andmay produce feature or representation as output, and the decoder may usethe extracted feature from the encoder as input and may reconstruct theoriginal input raw data as output. Training LSTM auto-encoder 112 can berepeated until some stopping criteria are satisfied. LSTM auto-encoder112 may invoke time series data 104 using auto encoding process toreduce dimensions of a sensor tag spaces to limited embedding space.LSTM auto-encoder 112 may invoke a clustering method to generatepotential modes. LSTM auto-encoder 112 may invoke a type of artificialneural network used to learn efficient data coding in an unsupervisedmanner. LSTM auto-encoder 112 may get a fixed sized vector from timeseries data 104. LSTM auto-encoder 112 may not only use the set offeatures for learning, but also learn the set of features itself. Theset of features may be information related to time series data 104. Theset of features may be an individual measurable property orcharacteristic of a phenomenon being observed from time series data 104.LSTM auto-encoder 112 may select a subset of relevant features forcreating prediction model 114 based on non-control variables 122defining no control by a user on time series data 104. LSTM auto-encoder112 may reduce the number of resources required to describe time seriesdata 104. LSTM auto-encoder 112 may construct combinations ofnon-control variables 122 describing time series data 104 withsufficient accuracy.

In one or more embodiments, prediction model 114 is configured todiscover an operational opportunity based on time series data 104. Theoperational opportunity may be identified through the comparison of thecurrent state with the historical similar operations inside the mode orneighborhood. In an example, the operational opportunity may be a set ofoperational changes deduced from the historical operational state toincrease the current operational state into a higher production in adefined short-term future period. In another example, the operationalopportunity may be a set of operational changes deduced from thehistorical operational state to reduce the current operational stateinto a low usage of additives or raw materials in a defined short-termfuture period. Other suitable opportunities are possible to be found.

In one or more embodiments, prediction model 114 is configured toidentify one or more operational modes based on the extracted featuresincluding a dimensional reduction with a representation learning fromtime series data 104. Prediction model 114 may identify a neighborhoodfor the current operational state. The neighborhood may be a dynamicmode within the same operational mode and may be found through Euclideandistances between the historical operational state and the currentoperational state. Prediction model 114 may define the neighborhood oruse Gaussian mixture clustering applied to embedded to identify staticoperational modes. A mode may include a status of plant operation. Inthe sand oil industry, there is a complex process of converting sand oilinto synthesis crude oil. To complete a synthesis crude oil productiongeneration, there are multiple stages involved, including primaryextraction, secondary extraction, and upgrading. Each stage involvesmultiple components and processes. Some of the involved modes may beexplicitly known by a site engineer. Other modes may be hidden and canbe identified through advanced analytic models. Prediction model 114 mayautomatically detect an operational mode by learning from historicalsensor and other production data and may achieve compact feedback in theform of modes. Prediction model 114 may use the operational modes as abenchmark if similar production conditions have been present in thepast, or if a new mode may be identified. Prediction model 114 may usethe operational modes to recommend better control actions or toautomatically change control parameters. Prediction model 114 maycompute historical benchmarking of a detected mode against historicalplant data when the plant lived in the same mode. Prediction model 114may use the detected mode as a basis for identifying the similar timeperiods from history. For instance, time periods from history may beidentified when the same mode was active. Prediction model 114 mayfurther use factor analysis on the set of process variables that jointlydefine the detected mode to identify the key difference in terms of thecurrent variable values versus the historical variable values.Prediction model 114 may compute and display to a site engineer on thepossible root cause of the low levels of operation/production.Prediction model 114 may provide an automatic process, providing asimpler human involvement approach to generate modes. Prediction model114 may achieve an opportunity realization through analysis usingunsupervised machine learning. For example, prediction model 114 mayidentify an operational opportunity through the comparison of thecurrent state with the historical similar operations inside the mode orneighborhood. Prediction model 114 may identify a specific mode wherethe current operational state resides. Prediction model 114 may projectclusters using t-SNE compression to generate a graph. T-SNE may be analgorithm for dimensionality reduction that is suited to visualizinghigh-dimensional data, e.g., time series data 104. Prediction model 114may embed high-dimensional points in low dimensions in a way thatrespects similarities between points with the t-SNE compression. In anexample, prediction model 114 achieve a high bitumen extraction bycomparing the current operational state with other operations located ata same mode. Prediction model 114 may analyze episodes with poorperformance and discover an operational opportunity to improve.Prediction model 114 may focus on these operation episodes in thehistory reached a high froth production and may extract key operationactions from these episodes to help the current operation.

In one or more embodiments, variable identification module 116 isconfigured to identify non-control variables 122 and control variables124 from time series data 104. Non-control variables 122 and controlvariables 124 may be separated to ensure the ability to take action togain production enhancement opportunities based on time series data 104.For example, non-control variables 122 may be time-stamped variables andmay be defined as a set of variables from sensors which users havelittle to no control over. Non-control variables 122 may be parametersthat are used to define how similar operation and production conditionsare. Non-control variables 122 may retrieve episodes from time seriesdata 104. Control variables 124 may be time-stamped variables and may bedefined as a set of variables from sensors for actions that can becontrolled by a user. In an example, control variables 124 can be usedto calculate rewards (or opportunities) from the episodes of the timeseries data. Control variables 124 may be production rates and rawmaterial variables that the user can optimize based on the bestneighboring episodes found. Control variables 124 may be identified froman established neighborhood of similar historical non-control variables122 to create possible action strategies that are relevant to thecurrent state based on time series data 104. In one or more embodiments,variable identification module 116 is configured to identify one or morecontrol variables 124 in the same mode which variables are relevant tothe current operational state.

In one or more embodiments, strategy recommendation module 118 isconfigured to recommend an action strategy based on control variables124, non-control variables 122, and a target productivity. Strategyrecommendation module 118 may define a similarity measurement toidentify historical episodes from time series data 104, with a similaroperational state based on the comparison of the current state with thehistorical operational state. Strategy recommendation module 118 maycreate an episode from the historical episodes that demonstrate a higherproductivity or throughput. Strategy recommendation module 118 maygenerate scores based on alternative action strategies and may recommendthe action strategy based on the scoring for each alternative actionstrategy.

In one or more embodiments, output module 120 is configured to outputthe action strategy for a user. Output module 120 may provide a userinterface to interface with the user. Output module 120 may provideother suitable output ways with the user. Output module 120 may providean alert to a user for a discovered operational opportunity. Outputmodule 120 may display operational modes using a t-SNE method. Outputmodule 120 may display a neighborhood episode using a time-stampedchart. Output module 120 may display an estimated gain of the actionstrategy.

FIG. 2 is a flowchart 200 depicting operational steps of opportunitydiscovery module 110 in accordance with an embodiment of the presentdisclosure.

Opportunity discovery module 110 operates to monitor time series data104 generated from one or more sensors. Opportunity discovery module 110also operates to extract a set of features from time series data 104,through autoencoding using a neural network, e.g., LSTM auto-encoder112, based on non-control variables 122 for time series data 104.Opportunity discovery module 110 operates to identify one or moreoperational modes based on the extracted features including adimensional reduction with a representation learning from time seriesdata 104. Opportunity discovery module 110 operates to compare a currentoperational state to a historical operational state based on time seriesdata 104 at a same operational mode of the operational modes.Opportunity discovery module 110 operate to discover an operationalopportunity based on the comparison of the current operational state tothe historical operational state. Opportunity discovery module 110operates to identify control variables 124 in the same mode whichvariables are relevant to the current operational state. Opportunitydiscovery module 110 operates to recommend an action strategy based oncontrol variables 124, non-control variables 122, and a targetproductivity. Opportunity discovery module 110 operates to outputs theaction strategy for a user.

In step 202, opportunity discovery module 110 monitors time series data104 generated from one or more sensors. For example, time series data104 can be data from an oil sand operation and production process.Petroleum products may be produced from oil sands through severalstages, e.g., extraction, upgrading, and refinement. For example, solidand water may be removed during the extraction stage which may extractbitumen from the oil sands. During the upgrading stage, bitumen may beupgraded to a lighter, intermediate crude oil product. During therefinement stage, crude oil may be refined into final products such asgasoline, lubricants and diluents. The processes during the stages mayinvolve multiple sequence steps of physical or chemical transformationto convert from one material to another. An optimal balance of processesis needed to reach multiple objectives within such production system. Inanother example, time series data 104 can be data from a steelmakingprocess of producing steel from iron ore and/or scrap. Impurities suchas nitrogen, silicon, phosphorus, sulfur and excess carbon may beremoved from the sourced iron, and alloying elements such as manganese,nickel, chromium, carbon and vanadium may be added to produce differentgrades of steel. In yet another example, the time series data can bedata from a production process which may process soybeans into soysauces with additives added to the soy sauces. In yet another example,the time series data can be data from any other suitable operation andproduction process.

In step 204, opportunity discovery module 110 extracts a set of featuresfrom time series data 104, through autoencoding using a neural network,e.g., LSTM auto-encoder 112, based on one or more non-control variables122 for time series data 104. Opportunity discovery module 110 may notonly use the set of features for learning, but also learn the set offeatures through LSTM auto-encoder 112. The set of features may beinformation related to time series data 104. The set of features may bean individual measurable property or characteristic of a phenomenonbeing observed from time series data 104. Opportunity discovery module110 may select a subset of relevant features for creating predictionmodel 114 based on non-control variables 122 defining a lack of controlby a user on time series data 104. Opportunity discovery module 110 mayreduce the number of resources required to describe time series data104. Opportunity discovery module 110 may construct combinations ofnon-control variables 122 describing time series data 104 withsufficient accuracy through LSTM auto-encoder 112. LSTM auto-encoder 112may be an artificial neural network used to learn efficient data codingin an unsupervised manner. LSTM auto-encoder 112 may be a recurrentneural network with an implementation of an autoencoder for time seriesdata 104 using an encoder-decoder LSTM architecture. LSTM auto-encoder112 may learn a representation (encoding) for time series data 104,e.g., for dimensionality reduction. LSTM auto-encoder 112 may generatefrom the reduced encoding a representation as close as possible to theoriginal input of time series data 104. LSTM auto-encoder 112 mayinclude an encoder and a decoder. The encoder may use raw data (e.g.,time series data 104) as input and may produce feature or representationas output, and the decoder may uses the extracted feature from theencoder as input and may reconstruct the original input raw data asoutput. Training can be repeated until some stopping criteria aresatisfied. LSTM auto-encoder 112 may invoke time series data 104 usingauto encoding process to reduce dimensions of a sensor tag space tolimited embedding space. LSTM auto-encoder 112 may invoke a clusteringmethod to generate potential modes. LSTM auto-encoder 112 may invoke atype of artificial neural network used to learn efficient data coding inan unsupervised manner. LSTM auto-encoder 112 may get a fixed sizedvector from time series data 104.

In step 206, opportunity discovery module 110 identifies one or moreoperational modes based on the extracted features including adimensional reduction with a representation learning from time seriesdata 104. Opportunity discovery module 110 may identify a neighborhoodfor the current operational state. The neighborhood may be a dynamicmode within the same operational mode and may be found through Euclideandistances between the historical operational state and the currentoperational state. Opportunity discovery module 110 may define theneighborhood or use Gaussian mixture clustering applied to embedded toidentify static operational modes. A mode may include a status of plantoperation. A hard category type of a mode may regard a specificoperational configuration of an entire production process, such asmanufacturing a pipe configuration, a unit operation status. A softcategory type of a mode may include a production level of local orglobal operations. In the sand oil industry, there is a complex processof converting sand oil into synthesis crude oil. To complete a synthesiscrude oil production generation, there are multiple stages involved,including primary extraction, secondary extraction, and upgrading. Eachstage involves multiple components and processes, and the system is adynamic system. Some of the involved modes may be explicitly known by asite engineer. Other modes may be hidden and can be identified throughadvanced analytic models. Opportunity discovery module 110 mayautomatically detect an operational mode by learning from historicalsensor and other production data and may achieve compact feedback in theform of modes. Opportunity discovery module 110 may determine theoperational mode algorithms and models. Opportunity discovery module 110may use the operational modes as a benchmark if similar productionconditions have been present in the past, or if a new mode may beidentified. The operational modes may be used to recommend bettercontrol actions or to automatically change control parameters. If a newmode is identified, the new mode may be saved to expand the memory andknowledge of the system. Opportunity discovery module 110 may computehistorical benchmarking of a detected mode against historical plant datawhen the plant is in the same mode. Opportunity discovery module 110 mayuse the detected mode as a basis for identifying the similar timeperiods from history. For instance, time periods from history may beidentified when the same mode is active. Opportunity discovery module110 may further use factor analysis on the set of process variables thatjointly define the detected mode to identify the key difference in termsof the current variable values versus the historical variable values.Opportunity discovery module 110 may compute and display to a siteengineer on the possible root cause of the low levels ofoperation/production. Opportunity discovery module 110 may provide ananalytic approach to identify or classify modes over a complexmanufacture process. Opportunity discovery module 110 may provide adetailed or general multivariate mode identification, and as abyproduct, may also achieve a partitioning of un-partitioned data intoseveral subsets. Rather than relying on a rule-based mode detection thatrequires prior knowledge and stored principles, opportunity discoverymodule 110 may provide an automatic process, providing a simpler humaninvolvement approach to generate modes. Opportunity discovery module 110may achieve an opportunity realization through analysis usingunsupervised machine learning. For example, opportunity discovery module110 may identify an operational opportunity through the comparison ofthe current state with the historical similar operations inside the modeor neighborhood.

In step 208, opportunity discovery module 110 compares a currentoperational state to a historical operational state based on the timeseries data at a same operational mode of the one or more operationalmodes. Opportunity discovery module 110 may identify a specific modewhere the current operational state resides. Opportunity discoverymodule 110 may project clusters using t-SNE compression to generate agraph. In an example, T-SNE may be an algorithm for dimensionalityreduction that is suited to visualizing high-dimensional data, e.g.,time series data 104. Opportunity discovery module 110 may embedhigh-dimensional points in low dimensions in a way that respectssimilarities between points with a t-SNE compression. In an example,opportunity discovery module 110 may achieve a high bitumen extractionby comparing the current operational state with other operations locatedat a same mode. Opportunity discovery module 110 may analyze episodeswith poor performance and may discover an operational opportunity toimprove. Opportunity discovery module 110 may focus on these operationepisodes in the history reached a high froth production and may extractkey operation actions from these episodes to help the current operation.

In step 210, opportunity discovery module 110 discovers an operationalopportunity based on the comparison of the current operational state tothe historical operational state. The operational opportunity may beidentified through the comparison of the current state with thehistorical similar operations inside the mode or neighborhood. In anexample, the operational opportunity may be a set of operational changesdeduced from the historical operational state to increase the currentoperational state into a higher production in a defined short-termfuture period. In another example, the operational opportunity may be aset of operational changes deduced from the historical operational stateto reduce the current operational state into a low usage of additives orraw materials in a defined short-term future period. Other suitableopportunities are possible to be found.

In step 212, opportunity discovery module 110 identifies one or morecontrol variables 124 in the same mode which variables are relevant tothe current operational state. Control variables 124 may define actionsthat can be controlled by a user. Control variables 124 may betime-stamped variables and can be defined as a set of variables fromsensors for actions that can be controlled by a user. In an example,control variables 124 can be used to calculate rewards (oropportunities) from the episodes of time series data 104. For example,control variables 124 may be production rates and raw material variablesthat the user can optimize based on the best neighboring episodes found.Control variables 124 may be identified from an established neighborhoodof similar historical non-control variables 122 to create possibleaction strategies that are relevant to the current state based on timeseries data 104. In a froth production example, control variables 124may include, for example, charge into diesel hydrotreating or catalytichydrogen treating, feedslate into diesel hydrotreating with low vacuumgas oil, low vacuum gas oil, side draw kerosene, heavy naphtha, andcoker kerosene.

In step 214, opportunity discovery module 110 recommends an actionstrategy based on control variables 124, non-control variables 122, anda target productivity. Opportunity discovery module 110 may define asimilarity measurement to identify historical episodes from time seriesdata 104, with a similar operational state based on the comparison ofthe current state with the historical operational state. Opportunitydiscovery module 110 may create an episode from the historical episodesthat demonstrate a higher productivity or throughput. Opportunitydiscovery module 110 may generate scores based on alternative actionstrategies and may recommend the action strategy based on the scoringfor each alternative action strategy.

In step 216, opportunity discovery module 110 outputs the actionstrategy for a user. Opportunity discovery module 110 may provide a userinterface to interface with the user. Opportunity discovery module 110may provide other suitable output ways with the user. Opportunitydiscovery module 110 may indicate a signal for the discoveredoperational opportunity. Opportunity discovery module 110 may displaythe operational modes using a t-SNE method. Opportunity discovery module110 may display a neighborhood episode using a time-stamped chart.Opportunity discovery module 110 may display an estimated gain of theaction strategy.

FIG. 3 illustrates an exemplary functional diagram of opportunitydiscovery module 110 in accordance with one or more embodiments of thepresent disclosure.

In the example of FIG. 3, opportunity discovery module 110 may achieveopportunity realization through analysis using unsupervised machinelearning. An operational opportunity may be identified through thecomparison of the current state (or scenario) with the historicalsimilar episodes inside a mode or neighborhood. Opportunity discoverymodule 110 may monitor operation as time series data 104 from sensors.Opportunity discovery module 110 may apply LSTM auto-encoder 112 toextract the feature of time series data 104 into an embedded space. LSTMauto-encoder 112 may achieve the time-series representation learningwith dimensional reduction. LSTM auto-encoder 112 may learn arepresentation (encoding) for time series data 104, e.g., fordimensionality reduction. LSTM auto-encoder 112 may generate from thereduced encoding a representation as close as possible to the originalinput of time series data 104. In the depicted embodiment, LSTMauto-encoder 112 includes encoder 320 and decoder 322. Encoder 320 maytake original input 324 (e.g., time series data 104) as input and mayproduce feature or representation as output. Decoder 322 may use theextracted feature from encoder 320 as input and may reconstruct originalinput 324 into reconstructed input 326 as output. Opportunity discoverymodule 110 may identify one or more static operational modes 302 (e.g.,mode 304) using Gaussian mixture clustering applied to the embeddedspace. Opportunity discovery module 110 may project the clusters usingt-SNE compression to generate graph of modes 302. Modes 302 may be usedas a benchmark against which the system may determine if similarproduction conditions have been present in the past. Modes 302 may beused to recommend better control actions or to automatically changecontrol parameters. Opportunity discovery module 110 may identify adynamic mode (e.g., neighborhood 308) for current operational state 306using the embedded space. Opportunity discovery module 110 may discoveroperational opportunities depending on, e.g., operational mode 304 orneighborhood 308. In an example, opportunity discovery module 110 maylimit historical episodes by selecting neighborhood 308 of currentoperational state 306 using the embedded space. Neighborhood 308 may befound through Euclidean distances between historical episodes andcurrent state 306 defined at the feature space. Block 310 demonstratesopportunity realization by comparing the current operational state withother operations located at the same mode, e.g., mode 304. An operationopportunity is demonstrated in opportunity window 312 as poor episodeswith poor performance may indicate an opportunity to improve.

FIG. 4 illustrates an exemplary architecture diagram of opportunitydiscovery module 110 in accordance with one or more embodiments of thepresent disclosure.

In block 402, a set of features can be selected to create predictionmodel 114 based on historical data 404, e.g., time series data 104.Prediction model 114 may identify a potential manufacture gain from lessusage of raw materials or additives without reducing production, orproduction increase of intermediate or final products with a same usageof raw materials or additives. Prediction model 114 may predictpotentials of less productive usage of raw materials or additives orproductivity increase with a same usage of raw materials or additives.Prediction model 114 may invoke opportunity identifying algorithms toidentify alternative operations to gain opportunities. Historical data404 may be data captured by one or more sensors. In an example,historical data 404 can be data from an oil sand operation andproduction process. In another example, historical data 404 can be datafrom a steelmaking process of producing steel from iron ore and/orscrap. In yet another example, historical data 404 can be data from anyother suitable operation and production process. Prediction model 114can be verified using control variables 124 and non-control variables122. Prediction model 114 may use non-control variables 122 (e.g.,environmental variables that a user has little or no control) toretrieve episodes from historical data 404 by neighborhood selection406. Prediction model 114 may use control variables 124 (e.g., actionvariables that a user may control or change in the process) to calculatethe rewards (or opportunities) from the episodes of historical data 404with cluster generation 408. Cluster generation 408 may group a set ofobjects from historical data 404 in such a way that objects in the samegroup (called a cluster) are more similar (in some sense) to each otherthan to those in other groups (clusters).

Alternative control strategies 410 can be action strategies to selectproper actions and change the environments to realize a finalopportunity. Alternative control strategies 410 may allow a user or aplan manager to gain a real time support for decision-making forincreasing productivity. For example, predictive model 114 may providescoring 412 and perform sorting 414 for list of recommendation 416 to auser. In an example, a user interface over a portable device may beprovided for a user. List of recommendation 416 of potential targetedquantities may be selected, e.g., from raw material, intermediate andfinal products, or expensive additives.

FIG. 5 depicts a block diagram 500 of components of computing device 102in accordance with an illustrative embodiment of the present disclosure.It should be appreciated that FIG. 5 provides only an illustration ofone implementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

Computing device 102 may include communications fabric 502, whichprovides communications between cache 516, memory 506, persistentstorage 508, communications unit 510, and input/output (I/O)interface(s) 512. Communications fabric 502 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric502 can be implemented with one or more buses or a crossbar switch.

Memory 506 and persistent storage 508 are computer readable storagemedia. In this embodiment, memory 506 includes random access memory(RAM). In general, memory 506 can include any suitable volatile ornon-volatile computer readable storage media. Cache 516 is a fast memorythat enhances the performance of computer processor(s) 504 by holdingrecently accessed data, and data near accessed data, from memory 506.

Opportunity discovery module 110 may be stored in persistent storage 508and in memory 506 for execution by one or more of the respectivecomputer processors 504 via cache 516. In an embodiment, persistentstorage 508 includes a magnetic hard disk drive. Alternatively, or inaddition to a magnetic hard disk drive, persistent storage 508 caninclude a solid state hard drive, a semiconductor storage device,read-only memory (ROM), erasable programmable read-only memory (EPROM),flash memory, or any other computer readable storage media that iscapable of storing program instructions or digital information.

The media used by persistent storage 508 may also be removable. Forexample, a removable hard drive may be used for persistent storage 508.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage508.

Communications unit 510, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 510 includes one or more network interface cards.Communications unit 510 may provide communications through the use ofeither or both physical and wireless communications links. Opportunitydiscovery module 110 may be downloaded to persistent storage 508 throughcommunications unit 510.

I/O interface(s) 512 allows for input and output of data with otherdevices that may be connected to computing device 102. For example, I/Ointerface 512 may provide a connection to external devices 518 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 518 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention, e.g., opportunity discovery module110 can be stored on such portable computer readable storage media andcan be loaded onto persistent storage 508 via I/O interface(s) 512. I/Ointerface(s) 512 also connect to display 520.

Display 520 provides a mechanism to display data to a user and may be,for example, a computer monitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Python, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

Although specific embodiments of the present invention have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the invention is not to belimited by the specific illustrated embodiments, but only by the scopeof the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:extracting, by one or more processors, a set of features from timeseries data, through autoencoding using a neural network, based on oneor more non-control variables for the time series data, the one or morenon-control variables defining a lack of control by a user on the timeseries data; identifying, by one or more processors, one or moreoperational modes based on the extracted features including adimensional reduction with a representation learning from the timeseries data; identifying, by one or more processors, a neighborhood of acurrent operational state based on the extracted features, theneighborhood being a dynamic mode within a same operational mode;comparing, by one or more processors, the current operational state tohistorical operational states based on the time series data at the sameoperational mode of the one or more operational modes; discovering, byone or more processors, an operational opportunity based on thecomparison of the current operational state to the historicaloperational states using the neighborhood; identifying, by one or moreprocessors, one or more control variables in the same mode whichvariables are relevant to the current operational state, the one or morecontrol variables defining actions that can be controlled by the user;and recommending, by one or more processors, an action strategy based onthe one or more control variables, the one or more non-controlvariables, and a target productivity.
 2. The computer-implemented methodof claim 1, further comprising: monitoring, by one or more processors,the time series data generated from one or more sensors.
 3. Thecomputer-implemented method of claim 1, wherein the neural network is along short-term memory auto-encoder.
 4. The computer-implemented methodof claim 1, wherein discovering the operational opportunity is based onthe comparison of the current operational state to the historicaloperational states using the mode.
 5. The computer-implemented method ofclaim 1, wherein the operational opportunity is selected from the groupconsisting of: a set of operational changes deduced from the historicaloperational state to increase the current operational state into ahigher production in a defined short-term future period, and a set ofoperational changes deduced from the historical operational state toreduce the current operational state into a low usage of additives orraw materials in a defined short-term future period.
 6. Thecomputer-implemented method of claim 1, further comprising: defining, byone or more processors, a similarity measurement to identify historicalepisodes from the time series data, with a similar operational statebased on the comparison of the current state with the historicaloperational state; and creating, by one or more processors, an episodefrom the historical episodes that demonstrates a selection from thegroup consisting of: higher productivity and throughput.
 7. Thecomputer-implemented method of claim 1, further comprising outputtingthe action strategy, the outputting comprising: providing an alert tothe user for the operational opportunity, displaying the one or moreoperational modes using a t-distributed stochastic neighbor embeddingmethod, displaying a neighborhood episode using a time-stamped chart,and displaying an estimated gain of the action strategy.
 8. A computerprogram product comprising: one or more computer readable storage media,and program instructions collectively stored on the one or more computerreadable storage media, the program instructions comprising: programinstructions to extract a set of features from time series data, throughautoencoding using a neural network, based on one or more non-controlvariables for the time series data, the one or more non-controlvariables defining a lack of control by a user on the time series data;program instructions to identify one or more operational modes based onthe extracted features including a dimensional reduction with arepresentation learning from the time series data; program instructionsto identify a neighborhood of a current operational state based on theextracted features, the neighborhood being a dynamic mode within a sameoperational mode; program instructions to compare the currentoperational state to historical operational states based on the timeseries data at the same operational mode of the one or more operationalmodes; program instructions to discover an operational opportunity basedon the comparison of the current operational state to the historicaloperational states using the neighborhood; program instructions toidentify one or more control variables in the same mode which variablesare relevant to the current operational state, the one or more controlvariables defining actions that can be controlled by the user; andprogram instructions to recommend an action strategy based on the one ormore control variables, the one or more non-control variables, and atarget productivity.
 9. The computer program product of claim 8, furthercomprising: program instructions, stored on the one or morecomputer-readable storage media, to monitor the time series datagenerated from one or more sensors.
 10. The computer program product ofclaim 8, wherein the neural network is a long short-term memoryauto-encoder.
 11. The computer program product of claim 8, whereinprogram instructions to discover the operational opportunity based onthe comparison of the current operational state to the historicaloperational states using the mode.
 12. The computer program product ofclaim 8, wherein the operational opportunity is selected from the groupconsisting of: a set of operational changes deduced from the historicaloperational state to increase the current operational state into ahigher production in a defined short-term future period, and a set ofoperational changes deduced from the historical operational state toreduce the current operational state into a low usage of additives orraw materials in a defined short-term future period.
 13. The computerprogram product of claim 8, further comprising: program instructions,stored on the one or more computer-readable storage media, to define asimilarity measurement to identify historical episodes from the timeseries data, with a similar operational state based on the comparison ofthe current state with the historical operational state; and programinstructions, stored on the one or more computer-readable storage media,to create an episode from the historical episodes that demonstrates aselection from the group consisting of: higher productivity andthroughput.
 14. The computer program product of claim 8, furthercomprising: program instructions, stored on the one or morecomputer-readable storage media, to output the action strategy, whereinprogram instructions to output comprise program instructions: to providean alert to the user for the operational opportunity, to display the oneor more operational modes using a t-distributed stochastic neighborembedding method, to display a neighborhood episode using a time-stampedchart, and to display an estimated gain of the action strategy.
 15. Acomputer system comprising: one or more computer processors, one or morecomputer readable storage media, and program instructions stored on theone or more computer readable storage media for execution by at leastone of the one or more computer processors, the program instructionscomprising: program instructions to extract a set of features from timeseries data, through autoencoding using a neural network, based on oneor more non-control variables for the time series data, the one or morenon-control variables defining a lack of control by a user on the timeseries data; program instructions to identify one or more operationalmodes based on the extracted features including a dimensional reductionwith a representation learning from the time series data; programinstructions to identify a neighborhood of a current operational statebased on the extracted features, the neighborhood being a dynamic modewithin a same operational mode; program instructions to compare thecurrent operational state to historical operational states based on thetime series data at the same operational mode of the one or moreoperational modes; program instructions to discover an operationalopportunity based on the comparison of the current operational state tothe historical operational states using the neighborhood; programinstructions to identify one or more control variables in the same modewhich variables are relevant to the current operational state, the oneor more control variables defining actions that can be controlled by theuser; and program instructions to recommend an action strategy based onthe one or more control variables, the one or more non-controlvariables, and a target productivity.
 16. The computer system of claim15, further comprising: program instructions, stored on the one or morecomputer-readable storage media, to monitor the time series datagenerated from one or more sensors.
 17. The computer system of claim 15,wherein the neural network is a long short-term memory auto-encoder. 18.The computer system of claim 15, wherein program instructions todiscover the operational opportunity based on the comparison of thecurrent operational state to the historical operational states using themode.
 19. The computer system of claim 15, wherein the operationalopportunity is selected from the group consisting of: a set ofoperational changes deduced from the historical operational state toincrease the current operational state into a higher production in adefined short-term future period, and a set of operational changesdeduced from the historical operational state to reduce the currentoperational state into a low usage of additives or raw materials in adefined short-term future period.
 20. The computer system of claim 15,further comprising: program instructions, stored on the one or morecomputer-readable storage media, to output the action strategy, whereinprogram instructions to output comprise program instructions: to providean alert to the user for the operational opportunity, to display the oneor more operational modes using a t-distributed stochastic neighborembedding method, to display a neighborhood episode using a time-stampedchart, and to display an estimated gain of the action strategy.